Understanding how abiotic conditions influence dispersal patterns of organisms is important for understanding the degree to which species can track and persist in the face of changing climate. The goal of this study was to understand how weather conditions influence the dispersal pattern of multiple nonmigratory grasshopper species from lower elevation grassland habitats in which they complete their life‐cycles to higher elevations that extend beyond their range limits. Using over a decade of weekly spring to late‐summer field survey data along an elevational gradient, we explored how abundance and richness of dispersing grasshoppers were influenced by temperature, precipitation, and wind speed and direction. We also examined how changes in population sizes at lower elevations might influence these patterns. We observed that the abundance of dispersing grasshoppers along the gradient declined 4‐fold from the foothills to the subalpine and increased with warmer conditions and when wind flow patterns were mild or in the downslope direction. Thirty‐eight unique grasshopper species from lowland sites were detected as dispersers across the survey years, and warmer years and weak upslope wind conditions also increased the richness of these grasshoppers. The pattern of grasshoppers along the gradient was not sex biased. The positive effect of temperature on dispersal rates was likely explained by an increase in dispersal propensity rather than by an increase in the density of grasshoppers at low elevation sites. The results of this study support the hypothesis that the dispersal patterns of organisms are influenced by changing climatic conditions themselves and as such, that this context‐dependent dispersal response should be considered when modeling and forecasting the ability of species to respond to climate change.
When humans assemble into face-to-face social networks, they create an extended environment that permits exposure to the microbiome of other members of a population. Social network interactions may thereby also shape the composition and diversity of the microbiome at individual and population levels. Here, we use comprehensive social network and detailed microbiome sequencing data in 1,098 adults across 9 isolated villages in Honduras to investigate the relationship between social network structure and microbiome composition. Using both species-level and strain-level data, we show that microbial sharing occurs between many relationship types, notably including non-familial and non-household connections. Using strain- sharing data alone, we can confidently predict a wide variety of relationship types (AUC ~0.73). This strain-level sharing extends to second-degree social connections in a network, suggesting the importance of the extended network with respect to microbiome composition. We also observe that socially central individuals are more microbially similar to the overall village than those on the social periphery. Finally, we observe that clusters of microbiome species and strains occur within clusters of people in the village social networks, providing the social niches in which microbiome biology and phenotypic impact are manifested.
Students learning the skills of science benefit from opportunities to move between the scientific problems and questions they confront and the mathematical tools available to answer the questions and solve the problems. Indeed, students learn science best when they are actively engaged in pursuing answers to authentic and relevant questions. We present an activity teachers can use in the classroom to introduce the concepts of species richness and diversity. We break down the history and logic behind the two primary statistical tools ecologists use to quantify species diversity: Simpson's and Shannon's diversity indices. With hypothetical data, we show how students can learn about and practice the calculations. We then describe an activity where students collect authentic ecological data with pitfall traps while learning some arthropod systematics and practicing their newly acquired quantitative reasoning skills, all within the context of edge effect ecology and habitat conservation. The entire activity reinforces for students how interesting and helpful mathematical models and quantitative reasoning in science can be for understanding biological phenomena, but also for generating more questions, and for designing additional data-collection techniques and experiments.
We propose JAWS, a series of wrapper methods for distribution-free uncertainty quantification tasks under covariate shift, centered on our core method JAW, the JAckknife+ Weighted with likelihood-ratio weights. JAWS also includes computationally efficient Approximations of JAW using higher-order influence functions: JAWA. Theoretically, we show that JAW relaxes the jackknife+'s assumption of data exchangeability to achieve the same finite-sample coverage guarantee even under covariate shift. JAWA further approaches the JAW guarantee in the limit of either the sample size or the influence function order under mild assumptions. Moreover, we propose a general approach to repurposing any distribution-free uncertainty quantification method and its guarantees to the task of risk assessment: a task that generates the estimated probability that the true label lies within a user-specified interval. We then propose JAW-R and JAWA-R as the repurposed versions of proposed methods for Risk assessment. Practically, JAWS outperform the state-of-the-art predictive inference baselines in a variety of biased real world data sets for both interval-generation and risk-assessment auditing tasks.
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